Monday.com report finds AI adoption lags, software use rises unevenly

The adoption gap is bigger than the tool count

monday.com’s latest World of Work report lands on a blunt workplace truth: companies can buy plenty of software, yet still fail to change how work actually gets done. The report finds that 82% of respondents use work and project management software, and 57% say they are using more tools than they did a year ago. Even with that much software in circulation, utilization can still fall in the largest enterprise environments, a sign that software sprawl does not automatically translate into better execution.

That is the hidden blocker for AI adoption too. The report suggests the bottleneck is not whether people have access to new tools, but whether they trust those tools enough to fold them into daily routines. For teams inside monday.com, that should read as a product lesson and a go-to-market lesson at the same time: adoption is built in operations, not just in features.

What the generational split really signals

One of the report’s most revealing findings is the divide between generations. Millennials in the survey adopted AI at a higher rate than Gen Z, which cuts against the easy assumption that younger workers will always be the most comfortable with new technology. The takeaway is less about age than about workplace context. Enthusiasm for technology does not automatically mean comfort with how that technology will change roles, expectations, or job security.

That matters because AI rollout often gets framed as a product launch when it is really a change-management event. Employees are not just deciding whether a tool is useful. They are deciding whether it is safe, understandable, and worth the effort of changing habits they already know how to use. In a company like monday.com, that distinction should shape how product, engineering, and sales talk about AI features internally and externally.

Why trust is the real product requirement

The report’s strongest message is that employees are more likely to embrace AI when they see it as something that enhances human work rather than replaces it. That is not a branding point. It is an adoption requirement. People want transparency about what a system does, what it does not do, and how their work will be affected before they rely on it.

For managers, that means the rollout cannot stop at a demo or a feature announcement. Workers need training, explicit usage rules, and proof that the tool improves something they feel every day, whether that is fewer manual updates, faster handoffs, or clearer visibility into priorities. If the company cannot show that value in practice, the tool becomes just another tab in an already crowded stack.

What engineers need to hear

For engineers, the report is a reminder that capable software is not the same thing as adopted software. A strong model or a smart workflow feature still depends on whether users understand it, trust it, and can predict its output. If people worry that AI will introduce errors, create surprise behavior, or obscure accountability, they will route around it.

That makes product transparency a technical issue, not just a communications issue. Teams building AI inside monday.com need to think about explainability, guardrails, and clear user controls as part of the product surface. The human question is simple: can a customer tell what the system is doing well enough to use it in front of their manager, their team, and their own performance review?

What product managers should measure

The report also pushes product managers to look past feature count and toward workflow change. A feature can be impressive and still fail if onboarding is weak or if the customer never converts trial usage into recurring habit. The report’s emphasis on work and project management software use, plus the drop in utilization in large enterprises, points to a familiar SaaS problem: implementation quality often determines ROI more than the software itself.

For PMs, the practical lesson is to measure more than activation. Look at whether a team keeps using a feature after the novelty fades, whether it changes the number of manual steps in a workflow, and whether managers can point to a visible outcome. If a customer buys monday.com to reduce coordination friction, the proof should be visible in fewer status-chasing messages, cleaner handoffs, and more reliable execution.

What successful rollout looks like

A good AI or software rollout should usually do four things:

• Explain the use case in plain language before asking for adoption.
• Train the people who will actually touch the workflow, not just the admin buyer.
• Set rules for where the tool should and should not be used.
• Track a small set of outcomes, such as time saved, task completion speed, or reduction in duplicated work.

That is how trust gets built operationally. Employees do not need a promise that a tool is transformative. They need evidence that it improves the work in front of them.

What sales teams should sell

The report also changes the sales conversation, especially in enterprise deals where tool sprawl is already a concern. If a buyer thinks they already have too many systems, the pitch cannot just be more software, more automation, or more AI. It has to be about better visibility, better coordination, and better outcomes from the tools they already expect people to use.

That is where implementation becomes part of the value proposition. Sales teams should be prepared to talk about how monday.com fits into existing routines, what change management looks like, and how the platform helps employees accept rather than resist new workflows. In practical terms, the argument is not that AI is trendy or that the platform is expansive. It is that adoption will hold because the product helps people do their jobs with less friction.

Career upside has to be visible too

The report also hints at another reason adoption stalls: people need to see a career upside, not just a productivity mandate. If a new tool only looks like extra monitoring or extra work, employees will treat it defensively. If it helps them move faster, demonstrate impact, or take on higher-value tasks, it has a better chance of sticking.

That is especially important inside SaaS companies, where product changes can ripple through support, customer success, sales, and engineering all at once. When teams can connect a new workflow to better results, cleaner data, or stronger customer conversations, the tool becomes part of professional growth rather than just another system to learn. For monday.com, that is the real lesson of the report: AI adoption is not won by louder messaging, but by clearer trust, better training, and proof that the work is actually improving.